Dynamic programming solves a big problem by combining answers to smaller overlapping subproblems, and it stores each small answer so it is computed only once. That reuse is the whole point.
It fits problems with two traits. First, optimal substructure: the best answer is built from best answers to smaller pieces. Second, overlapping subproblems: the same smaller pieces show up again and again during a plain recursive solve.
When both hold, naive recursion redoes the same work exponentially. Dynamic programming caches each subproblem result, turning that blow-up into something like O(n) or O(n*m) time, traded for memory to hold the table.
Typical fits are counting paths, shortest routes, and optimal splits. If a recursive solution keeps recomputing identical calls, that repetition is the signal to reach for it.
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